AbstractOver the past ten years many mathematical models of the human visual system (HVS) have been proposed for image processingapplications.1 -8 It has become clear that incorporating factors accounting for human perception can significantly improve overall picturequality. The purpose of this paper is to address the importance of applying the HVS model to image processing. In order to obtain good image quality at low bandwidth, it is proposed that a physiologically -based HVS model be incorporated with image compression systems. The physiological model needs no adjustment for input image as is required in the psychophysical model.Rationales relating the HVS model to image processingThree major rationales have been recognized for applying the HVS model to image processing. First, image bandwidth compressionrequires a basic understanding bf the HVS. For example, the eye compresses the image before sending visual signals to the brain.There are about 120 million rods and 7 million cones, which collect signals falling on the retina, but there are only 1 million optic nervefibers carrying signals out of ganglion cells to the brain.9 Furthermore, the eye can respond to scenes under background illuminationsthat range over nine orders of magnitude, while the impulse rate measured on optic fibers of the macaque monkey can vary only twoorders of magnitude.10 By treating image signals the same way as human vision is treated, it seems reasonable that better compressioncan be expected. For example, by incorporating the HVS model into the spatial transform coding technique, Faugeras7 has successfullycompressed color images from the original 27 bits /pixel to only 1 bit /pixel. With a better HVS model and a better image coding techni-que, we may be able to compress images at rates less than 1 bit /pixel.Second, human observers eventually judge the quality of processed images. So, it is important to find a good computable fidelity